用函数分支提升异常检测灵活性，让结果更可解释
Function Based Isolation Forest (FuBIF): A Unifying Framework for Interpretable Isolation-Based Anomaly Detection
- 用实值函数替代传统分割，动态构建决策树
- 支持多模型特征重要性分析，解释力更强
- 开源框架适合需要可解释性的工业场景
异常检测（AD）正朝着识别复杂数据集中的离群点方向发展。隔离森林（IF）作为关键的AD技术，存在适应性差和偏差问题。本文提出函数基础的隔离森林（FuBIF），对IF进行泛化，允许使用实值函数进行数据分支，显著提升了评估树构建的灵活性。同时，提出的FuBIF特征重要性（FuBIFFI）算法，为基于IF的模型提供跨多种可能模型的特征重要性评分，增强可解释性。本文详述了FuBIF的运行框架，评估其在多个基准方法上的性能，并探讨其理论贡献。开源实现已发布，以促进进一步研究并确保可复现性。
原文摘要 · Abstract (English)
Anomaly Detection (AD) is evolving through algorithms capable of identifying outliers in complex datasets. The Isolation Forest (IF), a pivotal AD technique, exhibits adaptability limitations and biases. This paper introduces the Function-based Isolation Forest (FuBIF), a generalization of IF that enables the use of real-valued functions for dataset branching, significantly enhancing the flexibility of evaluation tree construction. Complementing this, the FuBIF Feature Importance (FuBIFFI) algorithm extends the interpretability in IF-based approaches by providing feature importance scores across possible FuBIF models. This paper details the operational framework of FuBIF, evaluates its performance against established methods, and explores its theoretical contributions. An open-source implementation is provided to encourage further research and ensure reproducibility.
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